REVIEW 4 major objections 4 minor 96 references
Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields
T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash
Pith's one-line read Methylammonium effectively forbids the low-temperature phase transition in MAPbX3.
desk verdict A solid perovskite MLFF study with a credible A-site mechanism; the headline 'forbids' overreaches beyond finite-time kinetics, but the paper deserves serious peer review. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The argument runs on three coupled descriptors extracted from large-scale molecular dynamics. The first is the octahedral tilt field: tilting correlation polarity (TCP) along each principal axis, with values $-1$, $0$, $+1$ playing the role of Glazer superscripts, and a correlation-length tensor obtained by fitting the decay of tilt-tilt correlations to an exponential. The second is the molecular-orientation (MO) distribution of the organic cations, projected in a full octahedral-symmetry frame and in a no-symmetry frame, with a distribution variance that measures how concentrated the orientations are; the subset relations among $\alpha$, $\beta$, and $\gamma$ orientation sets are what make a transition free, guided, or limited. The third is the segregation parameter $k_{\mathrm{seg}}$, which counts how many bromine atoms surround each octahedron and quantifies the Monte Carlo-driven I/Br clustering. Together, these descriptors let the authors classify phases, locate transition temperatures from the crossing of angle-deviation curves, and attribute the MAPbX$_3$ arrest to molecular-orientation mismatch rather than to a bare static energy barrier.
What would settle it
A direct test would be a free-energy calculation, not just a cooling trajectory: use umbrella sampling or metadynamics on the same potentials to compute the $\beta\to\gamma$ free-energy barrier in MAPbX$_3$; if the barrier is only a few $k_{\mathrm{B}}T$ at 150 K, then the arrest is a finite-time artifact. On the experimental side, anneal a MAPbI$_3$ single crystal just below the $\beta$–$\gamma$ boundary for hours while monitoring neutron or X-ray diffuse scattering and molecular orientation; appearance of the $\gamma$-phase orientation pattern would refute the claim that the transition is effectively forbidden in accessible conditions.
Extended reading notes
Core claim
On the paper's own terms, the finding is that the A-site cation controls which symmetry-lowering pathways are kinetically accessible. For MAPbX$_3$, the $\gamma$-phase orientation distribution of the $\mathrm{MA}^+$ molecules is not a subset of the $\alpha$- or $\beta$-phase distributions, so the $\beta$-to-$\gamma$ transition demands extensive molecular rearrangement and crystal rotation; in the finite 12,000-atom cells and 0.2 K/ps cooling windows used here, it is effectively forbidden. The $\alpha$-to-$\beta$ transition remains easy because the $\beta$ orientation set is a subset of the $\alpha$ set. For FAPbX$_3$, the $\mathrm{FA}^+$ molecules keep the same $\langle100\rangle$ preference across all phases, so phase changes are free and the low-temperature phase is best represented as Im$\bar{3}$ with $a^+a^+a^+$ tilts, consistent with a recent diffuse-scattering experiment. Halide composition enters through tilt correlations: random I/Br mixing reduces the tilt correlation length below the linear interpolation of the pure endpoints, while partial segregation creates I-rich domains with unusually strong, anisotropic tilting and an anomalous $b^-$ mode that impedes coherent transformation. The paper further reports hysteresis between heating and cooling transitions, larger in mixed compositions, and $\Delta G_{\mathrm{mix}}$ estimates indicating FAPbX$_3$ is the most stable solid solution.
Load-bearing premise
The load-bearing premise is that the three trained force fields remain accurate when taken from small 2×2×2 training cells and short on-the-fly trajectories to production supercells of 12,000–13,720 atoms run at 0.2 K/ps heating and cooling; the authors openly note a systematic softening that already shifts transition temperatures below experiment, so if the potentials invent or suppress tilt instabilities at scale, the phase diagrams and the 'forbidden' transition claim shift with them.
Editorial extensions
If this is right
- If $\mathrm{MA}^+$ really forbids the $\beta$-to-$\gamma$ transition in finite crystals, then ordinary thermal cycling of MAPbI$_3$ leaves the material kinetically trapped in the tetragonal/cubic manifold, and the orthorhombic phase appears only with seeding, strain, or much slower cooling.
- If FAPbX$_3$'s low-temperature phase is Im$\bar{3}$ with $a^+a^+a^+$ tilts, then the FA system's transitions involve no molecular reorientation barrier, so its phase stability is governed almost entirely by octahedral tilting and is therefore most sensitive to force-field softening.
- If random halide mixing shortens tilt correlation lengths below the endpoint interpolation, then mixed I/Br compositions intrinsically suppress the $\alpha$-to-$\beta$ transition temperature relative to the pure halides, which the free-energy of mixing data corroborate.
- If segregated domains foster an anomalous $b^-$ tilt, then light- or temperature-induced halide segregation can change which crystallographic variant forms and even predetermine its orientation, coupling degradation directly to structural phase behavior.
Reading between the lines
- The orientation-subset criterion is a general screening rule: for any hybrid perovskite, compare the molecular orientation distributions of adjacent phases; when the lower-phase distribution is not a subset of the higher-phase one, expect kinetic arrest, regardless of the cation's identity. This could be tested cheaply on other organic cations such as aziridinium or ethylammonium.
- Because the paper's own softening caveat shifts absolute transition temperatures, the 'forbidden' claim is best read as a statement about kinetics in finite systems, not thermodynamics; enhanced-sampling simulations or microsecond runs on the same potentials might still nucleate $\gamma$ and would delimit how strong the barrier really is.
- The finding that segregation predetermines crystallization orientation suggests a feedback loop in operating devices: photoinduced halide demixing not only creates trap states but also selects the crystal orientation of the low-temperature phase, which may in turn influence ion migration and further segregation. This is a testable hypothesis with in-situ X-ray diffraction during illumination.
- The correlation-length metric could serve as a design proxy: compositions whose tilt correlation lengths are long and isotropic should transform more coherently, so screening candidates by tilt-correlation tensors may be a faster route to phase-stable alloys than enumerating full phase diagrams.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript trains three MACE machine-learning force fields on on-the-fly r2SCAN DFT data for CsPbX3, MAPbX3, and FAPbX3 and uses them in large-scale NpT molecular dynamics (up to ~13,700 atoms) with the PDynA toolkit to construct phase diagrams, extract octahedral tilt correlations, and analyze phase transformation pathways for pure and mixed-halide systems. The principal claims are that MA+ effectively forbids the beta-to-gamma transition in MAPbX3 because it requires extensive molecular reorientation and crystal rotation; that low-temperature FAPbX3 is best described as an Im-3 cubic phase with a+a+a+ tilts; and that halide segregation creates anomalous tilt modes that impede uniform transformations.
Significance. The paper is potentially significant for the halide-perovskite community: it demonstrates a practical workflow combining on-the-fly active learning, MACE potentials, and PDynA descriptors to reach length and time scales far beyond direct DFT, and it makes concrete, testable structural predictions, notably the Im-3 a+a+a+ phase for FAPbI3, which is compared with independent diffuse-scattering and neutron work. The local octahedral classification by halide configuration (Fig. 2) and the segregation-induced tilt analysis (Fig. 9) are valuable and go beyond bulk-averaged descriptors. However, the headline 'forbidden' transition is a finite-time kinetic observation, and the thermodynamic miscibility analysis is based on an overparameterized fit; these issues currently limit the strength of the conclusions that can be drawn.
major comments (4)
- [Abstract and Section III.C] The abstract states that MA+ effectively 'forbids' the beta-to-gamma transition in MAPbX3, but the body supports only a substantially weaker statement. Section III.C reports that when MA molecules are initially aligned as in the equilibrated gamma pattern, 'the gamma phase can be found in a wider window of temperatures and halide compositions' (Fig. 4, dashed line), and the conclusion itself adds the qualifier 'in a finite cell and time.' No free-energy barrier or nucleation rate is computed; the conclusion is an absence-of-events observation from eight constant-rate (0.2 K/ps) cooling runs on pristine supercells. The abstract should be qualified to state that beta-to-gamma is strongly suppressed under the simulated finite-cell, finite-time protocols, rather than categorically forbidden.
- [Section V and II.B] All central results depend on three bespoke MACE potentials, yet Section V only states that a repository with full training data and force-field parameters 'will also be provided,' without a current link or DOI. Without these data and parameters, the RMSEs in Table S1 cannot be checked and the transferability from 2x2x2 training cells (40-96 atoms) to production cells of 10x10x10-14x14x14 (12,000-13,720 atoms) cannot be assessed. This is especially important because the authors acknowledge a systematic softening of universal ML potentials that already shifts transition temperatures below experiment (Section III.A); the beta-to-gamma barrier inference inherits this bias. The potentials and training data should be released and, ideally, additional validation of large-scale tilt behavior (e.g., against DFT or experimental transition temperatures for several compositions) should be provided.
- [Section III.C, Eq. 7 and Fig. 8] The thermodynamic-stability analysis fits a fifth-order polynomial to seven enthalpy-of-mixing data points and then uses its second derivative to construct miscibility-gap and spinodal curves. A fifth-order polynomial has six coefficients, so the fit has essentially one degree of freedom; the resulting common-tangent construction is extremely sensitive to the polynomial form and no uncertainty or cross-validation is reported. The statement that 'the FA system possesses the highest stability among the three solid solutions' (Section III.C) therefore rests on a fragile numerical procedure. Either a lower-order fit with error estimates, a different thermodynamic model, or explicit validation against the cited DFT predictions is needed to support the quantitative stability ranking.
- [Section III.C (phase identification)] The phase classification underlying Fig. 4 uses criteria of 'insignificant tilt angle or a TCP value near zero' without reporting the numerical thresholds. Because every phase assignment and transition temperature in the equilibrium phase diagrams is derived from this thresholding, the maps could change substantially under reasonable alternative cutoffs. The authors should state the thresholds and demonstrate that the reported phase boundaries are robust to them.
minor comments (4)
- [Section II.B] The text contains a typo: 'structural descritor' should be 'structural descriptor.'
- [Fig. 6 caption] The caption contains a typo: 'crystal srtucture' should be 'crystal structure.'
- [Section V] The text 'force field paramaters' contains a typo; it should read 'force-field parameters.'
- [Equation (6)] The reference tilt angles \hat{\theta}_i^j are said to be obtained from equilibration calculations, but the exact procedure (which time windows, which compositions, and how averages are taken) is not specified; please clarify.
Circularity Check
No load-bearing circularity: the MLFF is trained on DFT energies/forces, not on phase labels, and the phase/transition claims are checked against external benchmarks; remaining self-citations are minor and non-load-bearing.
full rationale
The derivation chain is self-contained against external benchmarks. The MACE potentials are trained on DFT energies, forces, and stresses collected on-the-fly in 2x2x2 cells; phase labels and transition temperatures are not training targets, so the predicted phase diagrams are not fitted inputs renamed as predictions. Phase classification uses the independent PDynA tilt metrics (TCP, tilt angles, correlation lengths), and the heating/cooling transition temperatures are compared with experimental references and with equilibration-derived diagrams. The central claim about the MAPbX3 beta-to-gamma transition is explicitly qualified in Section IV as 'effectively forbidden in a finite cell and time'; Section III.C further admits that gamma-aligned initial MA orientations widen the gamma-phase window, so the abstract's stronger wording is an overstatement of a kinetic finite-time result, not a circular reduction. Self-citations to the PDynA toolkit (ref 74) and to the group's experimental FAPbI3 study (ref 35) involve overlapping authors, but they are not load-bearing: PDynA is a descriptor analysis package, and ref 35 is a scattering/neutron measurement rather than an output of the MLFF. No equation reduces to another by construction, and no fitted parameter is relabelled as a prediction.
Assumptions & free parameters
free parameters (3)
- MACE neural-network weights (three separate models) =
trained on 7,080 to 18,000 r2SCAN DFT snapshots per system
- Phase reference tilt angles theta_hat_j_i in Eq. 6 =
extracted from equilibration MD runs
- Fifth-order polynomial coefficients for Delta_Hmix(x) =
fitted to seven computed Delta_Hmix values per system
assumptions (7)
- domain assumption r2SCAN DFT with PAW, 550 eV cutoff, and a 2x2x2 k-grid is an accurate reference for tilt energetics
- standard math Exponential decay of tilt correlations (Eq. 5) adequately represents R_alpha,beta(k)
- domain assumption Ideal entropy of mixing Delta_Smix = -kB(xA ln xA + xB ln xB) for the halide sublattice
- domain assumption MLFF trained on small-cell on-the-fly data transfers to 12,000 to 13,720-atom supercells and 0.2 K/ps thermal ramps
- ad hoc to paper Phase-identification thresholds (insignificant tilt angle or TCP near zero) are appropriate
- ad hoc to paper Monte Carlo swap acceptance that only increases local halide concentration produces representative segregation states
- ad hoc to paper Heating/cooling rate of 0.2 K/ps allows representative nucleation observation
Cite this review
Pith. "Pith review of Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields." pith.science (2026). https://pith.science/paper/YD46BSMX
@misc{pith2026250707926,
author = {Pith},
title = {Pith review of: Phase Stability and Transformations in Lead Mixed Halide Perovskites from Machine Learning Force Fields},
year = {2026},
howpublished = {\url{https://pith.science/paper/YD46BSMX}},
note = {Machine review of arXiv:2507.07926}
}
abstract
Lead halide perovskites (APbX$_3$) offer tunable optoelectronic properties but feature an intricate phase-stability landscape. Here we employ on-the-fly data collection and an equivariant message-passing neural-network potential to perform large-scale molecular dynamics of three prototypical perovskite systems: CsPbX$_3$, MAPbX$_3$, and FAPbX$_3$. Integrating these simulations with the PDynA analysis toolkit, we resolve both equilibrium phase diagrams and dynamic structural evolution under varying temperature and halide-mixing conditions. Our findings reveal that the A-site cation strongly modulates octahedral tilt modes and phase pathways: MA$^+$ effectively "forbids" the beta-to-gamma transition in MAPbX$_3$ by requiring extensive molecular rearrangements and crystal rotation, whereas the debated low-temperature phase in FAPbX$_3$ is best represented as an Im$\bar{3}$ cubic phase with $a^+a^+a^+$ tilts. Additionally, small changes in halide composition and arrangement $\unicode{x2013}$ from uniform mixing to partial segregation $\unicode{x2013}$ alter tilt correlations. Segregated domains can even foster anomalous tilting modes that impede uniform phase transformations. These results highlight the multi-scale interplay between cation environment and halide distribution, offering a rational basis for tuning perovskite architectures toward improved phase stability.
Figures
Figures from the paper (6 more)
Reference graph
Works this paper leans on
-
[1]
Crystalstructureand photoconductivityof caesiumplumbohalides
Møller,C.K. Crystalstructureand photoconductivityof caesiumplumbohalides. Nature 1958, 182, 1436–1436
1958
-
[2]
CH3NH3PbX3, ein Pb(II)-system mit kubischer perowskitstruktur/CH3NH3PbX3, a Pb (II)-system with cubic perovskite structure.Zeitschrift für Naturforschung B1978, 33, 1443–1445
Weber, D. CH3NH3PbX3, ein Pb(II)-system mit kubischer perowskitstruktur/CH3NH3PbX3, a Pb (II)-system with cubic perovskite structure.Zeitschrift für Naturforschung B1978, 33, 1443–1445
-
[3]
An overview on enhancing the stability of lead halide perovskite quantum dots and their applications in phosphor-converted LEDs.Chemical Society Reviews 2019, 48, 310–350
Wei, Y.; Cheng, Z.; Lin, J. An overview on enhancing the stability of lead halide perovskite quantum dots and their applications in phosphor-converted LEDs.Chemical Society Reviews 2019, 48, 310–350
2019
-
[4]
S.; Yantara, N.; Liu, X.; Sabba, D.; Grätzel, M.; Mhaisalkar, S.; Sum, T
Xing, G.; Mathews, N.; Lim, S. S.; Yantara, N.; Liu, X.; Sabba, D.; Grätzel, M.; Mhaisalkar, S.; Sum, T. C. Low-temperature solution-processed wavelength-tunable perovskites for lasing. Nature Materials 2014, 13, 476–480
2014
-
[5]
Sensing studies and appli- cations based on metal halide perovskite materials: current advances and future perspectives
Huang, Y.; Feng, Y.; Li, F.; Lin, F.; Wang, Y.; Chen, X.; Xie, R. Sensing studies and appli- cations based on metal halide perovskite materials: current advances and future perspectives. TrAC Trends in Analytical Chemistry2021, 134, 116127
-
[6]
Organometal halide perovskites as visible- light sensitizers for photovoltaic cells.Journal of the American Chemical Society2009, 131, 6050–6051
Kojima, A.; Teshima, K.; Shirai, Y.; Miyasaka, T. Organometal halide perovskites as visible- light sensitizers for photovoltaic cells.Journal of the American Chemical Society2009, 131, 6050–6051
-
[7]
J.; Noh, J
Jeon, N. J.; Noh, J. H.; Yang, W. S.; Kim, Y. C.; Ryu, S.; Seo, J.; Seok, S. I. Compositional engineering of perovskite materials for high-performance solar cells.Nature 2015, 517, 476– 480
2015
-
[8]
Sahli, F. et al. Fully textured monolithic perovskite/silicon tandem solar cells with 25.2% power conversion efficiency.Nature Materials 2018, 17, 820–826
2018
Show all 96 references
-
[9]
M.; Butler, K
Frost, J. M.; Butler, K. T.; Brivio, F.; Hendon, C. H.; Van Schilfgaarde, M.; Walsh, A. Atomistic origins of high-performance in hybrid halide perovskite solar cells.Nano Letters 2014, 14, 2584–2590
2014
-
[10]
Unusual defect physics in CH3NH3PbI3 perovskite solar cell absorber
Yin, W.-J.; Shi, T.; Yan, Y. Unusual defect physics in CH3NH3PbI3 perovskite solar cell absorber. Applied Physics Letters2014, 104, 063903
-
[11]
Light Absorption Coefficient of CsPbBr3 Perovskite Nanocrystals.The 30 Journal of Physical Chemistry Letters2018, 9, 3093–3097
Maes, J.; Balcaen, L.; Drijvers, E.; Zhao, Q.; De Roo, J.; Vantomme, A.; Vanhaecke, F.; Geiregat, P.; Hens, Z. Light Absorption Coefficient of CsPbBr3 Perovskite Nanocrystals.The 30 Journal of Physical Chemistry Letters2018, 9, 3093–3097
-
[12]
K.; Gratzel, M.; De Angelis, F
Amat, A.; Mosconi, E.; Ronca, E.; Quarti, C.; Umari, P.; Nazeeruddin, M. K.; Gratzel, M.; De Angelis, F. Cation-induced band-gap tuning in organohalide perovskites: interplay of spin–orbit coupling and octahedra tilting.Nano Letters 2014, 14, 3608–3616
2014
-
[13]
Intrinsic thermal instability of methylam- monium lead trihalide perovskite.Advanced Energy Materials2015, 5, 1500477
Conings, B.; Drijkoningen, J.; Gauquelin, N.; Babayigit, A.; D’Haen, J.; D’Olieslaeger, L.; Ethirajan, A.; Verbeeck, J.; Manca, J.; Mosconi, E. Intrinsic thermal instability of methylam- monium lead trihalide perovskite.Advanced Energy Materials2015, 5, 1500477
-
[14]
Advanced Materials 2019, 31, 1805337
Park,B.; Seok,S.I.Intrinsicinstabilityofinorganic–organichybridhalideperovskitematerials. Advanced Materials 2019, 31, 1805337
2019
-
[15]
A.; Makarov, S
Verkhogliadov, G.; Haroldson, R.; Gets, D.; Zakhidov, A. A.; Makarov, S. V. Temperature Dependence of Photoinduced Phase Segregation in Bromide-Rich Mixed Halide Perovskites. The Journal of Physical Chemistry C2023, 127, 24339–24349
-
[16]
Halide Segregated Crystallization of Mixed-Halide Perovskites Revealed by In Situ GIWAXS.ACS Applied Materials & Interfaces 2024, 16, 8913–8921
Merten, L.; Eberle, T.; Kneschaurek, E.; Scheffczyk, N.; Zimmermann, P.; Zaluzhnyy, I.; Khadiev, A.; Bertram, F.; Paulus, F.; Hinderhofer, A. Halide Segregated Crystallization of Mixed-Halide Perovskites Revealed by In Situ GIWAXS.ACS Applied Materials & Interfaces 2024, 16, 8913–8921
2024
-
[17]
A.; Winchester, A
Doherty, T. A.; Winchester, A. J.; Macpherson, S.; Johnstone, D. N.; Pareek, V.; Ten- nyson, E. M.; Kosar, S.; Kosasih, F. U.; Anaya, M.; Abdi-Jalebi, M. Performance-limiting nanoscale trap clusters at grain junctions in halide perovskites.Nature 2020, 580, 360–366
2020
-
[18]
C.; Kanatzidis, M
Marronnier, A.; Roma, G.; Boyer-Richard, S.; Pedesseau, L.; Jancu, J.-M.; Bonnassieux, Y.; Katan, C.; Stoumpos, C. C.; Kanatzidis, M. G.; Even, J. Anharmonicity and Disorder in the Black Phases of Cesium Lead Iodide Used for Stable Inorganic Perovskite Solar Cells.ACS Nano 201...
2018
-
[19]
Elastic Constants, Optical Phonons, and Molecular Relaxations in the High Temperature Plastic Phase of the CH3NH3PbBr3 Hybrid Perovskite
Létoublon, A.; Paofai, S.; Rufflé, B.; Bourges, P.; Hehlen, B.; Michel, T.; Ecolivet, C.; Du- rand, O.; Cordier, S.; Katan, C.; Even, J. Elastic Constants, Optical Phonons, and Molecular Relaxations in the High Temperature Plastic Phase of the CH3NH3PbBr3 Hybrid Perovskite. Th...
-
[20]
K.; Liang, X.; Balciunas, S.; Semenikhin, O
Maczka, M.; Ptak, M.; Gagor, A.; Zareba, J. K.; Liang, X.; Balciunas, S.; Semenikhin, O. A.; Kucheriv, O. I.; Gural’skiy, I. A.; Shova, S.; Walsh, A.; Banys, J.; Šim˙ enas, M. Phase Tran- sitions, Dielectric Response, and Nonlinear Optical Properties of Aziridinium Lead Halide...
2023
-
[21]
Z.; Waqas, M.; Leung, T
Li, Y.; Liu, F. Z.; Waqas, M.; Leung, T. L.; Tam, H. W.; Lan, X. Q.; Tu, B.; Chen, W.; Djurišić, A. B.; He, Z. B. Formamidinium-Based Lead Halide Perovskites: Structure, Proper- ties, and Fabrication Methodologies.Small Methods 2018, 2, 1700387
2018
-
[22]
A thin film (< 200 nm) perovskite solar cell with 18% efficiency.Journal of Materials Chemistry A2020, 8, 17420–17428
Zhang, Y.; Park, N.-G. A thin film (< 200 nm) perovskite solar cell with 18% efficiency.Journal of Materials Chemistry A2020, 8, 17420–17428
-
[23]
Three-Dimensional Methylhydrazinium Lead Halide Perovskites: Structural Changes and Ef- fects on Dielectric, Linear, and Nonlinear Optical Properties Entailed by the Halide Tuning
Drozdowski,D.; Gagor,A.; Stefanska,D.; Zareba,J.K.; Fedoruk,K.; Maczka,M.; Sieradzki,A. Three-Dimensional Methylhydrazinium Lead Halide Perovskites: Structural Changes and Ef- fects on Dielectric, Linear, and Nonlinear Optical Properties Entailed by the Halide Tuning. The Jour...
-
[24]
Absolute energy level positions in tin-and lead-based halide perovskites.Nature Communications 2019, 10, 2560
Tao, S.; Schmidt, I.; Brocks, G.; Jiang, J.; Tranca, I.; Meerholz, K.; Olthof, S. Absolute energy level positions in tin-and lead-based halide perovskites.Nature Communications 2019, 10, 2560
2019
-
[25]
Temperature-Dependent Optical Band Gap in CsPbBr3, MAPbBr3, and FAPbBr3 Single Crys- tals
Mannino, G.; Deretzis, I.; Smecca, E.; La Magna, A.; Alberti, A.; Ceratti, D.; Cahen, D. Temperature-Dependent Optical Band Gap in CsPbBr3, MAPbBr3, and FAPbBr3 Single Crys- tals. The Journal of Physical Chemistry Letters2020, 11, 2490–2496
-
[26]
H.; Im, S
Noh, J. H.; Im, S. H.; Heo, J. H.; Mandal, T. N.; Seok, S. I. Chemical Management for Colorful, Efficient, and Stable Inorganic–Organic Hybrid Nanostructured Solar Cells.Nano Letters 2013, 13, 1764–1769
2013
-
[27]
S.; Noh, J
Yang, W. S.; Noh, J. H.; Jeon, N. J.; Kim, Y. C.; Ryu, S.; Seo, J.; Seok, S. I. High-performance photovoltaic perovskite layers fabricated through intramolecular exchange.Science 2015, 348, 1234–1237
2015
-
[28]
I.; Garriga, M.; Campoy-Quiles, M.; Weller, M
Francisco-López, A.; Charles, B.; Alonso, M. I.; Garriga, M.; Campoy-Quiles, M.; Weller, M. T.; Goñi, A. R. Phase Diagram of Methylammonium/Formamidinium Lead Io- dide Perovskite Solid Solutions from Temperature-Dependent Photoluminescence and Raman Spectroscopies. The Journal...
-
[29]
Phase Transitions and Dynamics in Mixed Three- and Low-Dimensional Lead Halide Perovskites.Chemical Reviews 2024, 124, 2281– 2326
Simenas, M.; Gagor, A.; Banys, J.; Maczka, M. Phase Transitions and Dynamics in Mixed Three- and Low-Dimensional Lead Halide Perovskites.Chemical Reviews 2024, 124, 2281– 2326
2024
-
[30]
First-principles identification of the charge-shifting mechanism and ferroelectricity in hybrid halide perovskites.Scientific Reports 2020, 10
Kim, B.; Kim, J.; Park, N. First-principles identification of the charge-shifting mechanism and ferroelectricity in hybrid halide perovskites.Scientific Reports 2020, 10
2020
-
[31]
F.; Mora-Seró, I
Masi, S.; Gualdrón-Reyes, A. F.; Mora-Seró, I. Stabilization of Black Perovskite Phase in 32 FAPbI3 and CsPbI3. ACS Energy Letters2020, 5, 1974–1985
1974
-
[32]
H.; Stoumpos, C
Fabini, D. H.; Stoumpos, C. C.; Laurita, G.; Kaltzoglou, A.; Kontos, A. G.; Falaras, P.; Kanatzidis, M. G.; Seshadri, R. Reentrant structural and optical properties and large positive thermal expansion in perovskite formamidinium lead iodide.Angewandte Chemie 2016, 128, 15618–15622
2016
-
[33]
J.; Lee, J.; Ruff, J
Chen, T.; Chen, W.-L.; Foley, B. J.; Lee, J.; Ruff, J. P.; Ko, J. P.; Brown, C. M.; Har- riger, L. W.; Zhang, D.; Park, C. Origin of long lifetime of band-edge charge carriers in organic–inorganic lead iodide perovskites. Proceedings of the National Academy of Sciences 2017, 1...
2017
-
[34]
J.; Liang, X.; Klarbring, J.; Dubajic, M.; Dell’Angelo, D.; Sutton, C.; Caddeo, C.; Stranks, S
Baldwin, W. J.; Liang, X.; Klarbring, J.; Dubajic, M.; Dell’Angelo, D.; Sutton, C.; Caddeo, C.; Stranks, S. D.; Mattoni, A.; Walsh, A.; Csányi, G. Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains.Small 2024, 20, 2303565
2024
-
[35]
R.; Klarbring, J.; Liang, X.; Boer, S
Dubajic, M.; Neilson, J. R.; Klarbring, J.; Liang, X.; Boer, S. A.; Rule, K. C.; Auckett, J. E.; Gu, L.; Jia, X.; Pusch, A. Dynamic Nanodomains Dictate Macroscopic Properties in Lead Halide Perovskites.arXiv preprint arXiv:2404.14598 2024,
2024 arXiv
-
[36]
Doherty, T. A. S. et al. Stabilized tilted-octahedra halide perovskites inhibit local formation of performance-limiting phases.Science 2021, 374, 1598–1605
2021
-
[37]
Weadock, N. J. et al. The Nature of dynamic local order in CH3NH3PbI3 and CH3NH3PbBr3. Joule 2023, 7, 1051–1066
2023
-
[38]
P.; Sadoughi, G.; Rehman, W.; Eperon, G
McMeekin, D. P.; Sadoughi, G.; Rehman, W.; Eperon, G. E.; Saliba, M.; Hörantner, M. T.; Haghighirad, A.; Sakai, N.; Korte, L.; Rech, B.; Johnston, M. B.; Herz, L. M.; Snaith, H. J. A mixed-cation lead mixed-halide perovskite absorber for tandem solar cells.Science 2016, 351, 151–155
2016
-
[39]
Z.; Song, T.-B.; Wang, H.-H.; Xu, X.; Liu, Y.; Lu, S.; You, J
Chen, Q.; Zhou, H.; Fang, Y.; Stieg, A. Z.; Song, T.-B.; Wang, H.-H.; Xu, X.; Liu, Y.; Lu, S.; You, J. The optoelectronic role of chlorine in CH3NH3PbI3(Cl)-based perovskite solar cells. Nature Communications 2015, 6, 7269
2015
-
[40]
I.; Krieg, F.; Caputo, R.; Hendon, C
Protesescu, L.; Yakunin, S.; Bodnarchuk, M. I.; Krieg, F.; Caputo, R.; Hendon, C. H.; Yang, R. X.; Walsh, A.; Kovalenko, M. V. Nanocrystals of cesium lead halide perovskites (CsPbX3, X= Cl, Br, and I): novel optoelectronic materials showing bright emission with wide color gamu...
2015
-
[41]
Huang, Z.; Jiang, F.; Song, Z.; Dolia, K.; Zhu, T.; Yan, Y.; Ginger, D. S. Local A-Site Phase 33 Segregation Leads to Cs-Rich Regions Showing Accelerated Photodegradation in Mixed-Cation Perovskite Semiconductor Films.ACS Energy Letters2024, 9, 3066–3073
-
[42]
Mechanisms of UV-Induced Degradation in Wide-Bandgap Per- ovskite Solar Cells: Insights from Microscopic Analysis.ACS Applied Energy Materials2024, 7, 11670–11677
Lang, X.; Gao, Z.; Zhao, Y.; Jiang, Y.; Liu, X.; Li, M.; Gou, Y.; Chen, C.; Zhao, D.; Wang, C.; Han, X.; Ye, J.; Xiao, C. Mechanisms of UV-Induced Degradation in Wide-Bandgap Per- ovskite Solar Cells: Insights from Microscopic Analysis.ACS Applied Energy Materials2024, 7, 11670–11677
-
[43]
M.; Bernard, G
Karmakar, A.; Askar, A. M.; Bernard, G. M.; Terskikh, V. V.; Ha, M.; Patel, S.; Shankar, K.; Michaelis, V. K. Mechanochemical Synthesis of Methylammonium Lead Mixed–Halide Per- ovskites: Unraveling the Solid-Solution Behavior Using Solid-State NMR.Chemistry of Mate- rials 2018...
2018
-
[44]
A.; Bokdam, M.; Grüninger, H.; Kentgens, A
Fykouras, K.; Lahnsteiner, J.; Leupold, N.; Tinnemans, P.; Moos, R.; Panzer, F.; de Wijs, G. A.; Bokdam, M.; Grüninger, H.; Kentgens, A. P. Disorder to order: how halide mixing in MAPbI3−xBrx perovskites restricts MA dynamics.Journal of Materials Chemistry A 2023, 11, 4587–4597
2023
-
[45]
M.; Chen, B.; Laquai, F.; Kanatzidis, M
Grater, L.; Wang, M.; Teale, S.; Mahesh, S.; Maxwell, A.; Liu, Y.; Park, S. M.; Chen, B.; Laquai, F.; Kanatzidis, M. G.; Sargent, E. H. Sterically Suppressed Phase Segregation in 3D Hollow Mixed-Halide Wide Band Gap Perovskites.The Journal of Physical Chemistry Letters 2023, 1...
2023
-
[46]
A.; M Soufiani, A.; Hao, X.; Yun, J
Mussakhanuly, N.; Choi, E.; L Chin, R.; Wang, Y.; Seidel, J.; Green, M. A.; M Soufiani, A.; Hao, X.; Yun, J. S. Multifunctional Surface Treatment against Imperfections and Halide Seg- regation in Wide-Band Gap Perovskite Solar Cells.ACS Applied Materials & Interfaces2024, 16, ...
-
[47]
Akash, S.; Pasha, A.; Balakrishna, R. G. Dissipation of Charge Accumulation and Suppression of Phase Segregation in Mixed Halide Perovskite Solar Cells via Nanoribbons.ACS Applied Energy Materials 2022, 5, 2727–2737
2022
-
[48]
Modeling the Photoelectrochemical Evolution of Lead-Based, Mixed- Halide Perovskites Due to Photosegregation.ACS Nano 2023, 17, 20502–20511
Ruth, A.; Kuno, M. Modeling the Photoelectrochemical Evolution of Lead-Based, Mixed- Halide Perovskites Due to Photosegregation.ACS Nano 2023, 17, 20502–20511
2023
-
[49]
Photoremixing of Photosegregated Formami- dinium/Cesium Lead Iodide/Bromide Thin Films under Pulsed Laser Excitation.ACS Energy Letters 2024, 9, 5744–5746
Okrepka, H.; Franzini, L.; Pagliara, S.; Kuno, M. Photoremixing of Photosegregated Formami- dinium/Cesium Lead Iodide/Bromide Thin Films under Pulsed Laser Excitation.ACS Energy Letters 2024, 9, 5744–5746
2024
-
[50]
The Journal of Physical Chemistry Letters2025, 1760–1768
Ghorai,A.; Singh,S.; Roy,B.; Bose,S.; Mahato,S.; Mukhin,N.; Jha,P.; Ray,S.K.Suppression 34 of Light-Induced Phase Segregations in Mixed Halide Perovskites through Ligand Passivation. The Journal of Physical Chemistry Letters2025, 1760–1768
-
[51]
E.; Slotcavage, D
Beal, R. E.; Slotcavage, D. J.; Leijtens, T.; Bowring, A. R.; Belisle, R. A.; Nguyen, W. H.; Burkhard, G. F.; Hoke, E. T.; McGehee, M. D. Cesium Lead Halide Perovskites with Improved Stability for Tandem Solar Cells.The Journal of Physical Chemistry Letters2016, 7, 746–751
-
[52]
Grau-Crespo, R.; Hamad, S.; Catlow, C. R. A.; De Leeuw, N. Symmetry-adapted configura- tional modelling of fractional site occupancy in solids.Journal of Physics: Condensed Matter 2007, 19, 256201
2007
-
[53]
Zunger, A.; Wei, S.-H.; Ferreira, L.; Bernard, J. E. Special quasirandom structures.Physical Review Letters 1990, 65, 353
1990
-
[54]
Atomistic models of metal halide perovskites.Matter 2021, 4, 3867–3873
Walsh, A. Atomistic models of metal halide perovskites.Matter 2021, 4, 3867–3873
2021
-
[55]
D.; Frost, J
Whalley, L. D.; Frost, J. M.; Jung, Y.-K.; Walsh, A. Perspective: Theory and simulation of hybrid halide perovskites.The Journal of Chemical Physics2017, 146
-
[56]
M.; Walsh, A
Frost, J. M.; Walsh, A. What Is Moving in Hybrid Halide Perovskite Solar Cells?Accounts of Chemical Research2016, 49, 528–535
-
[57]
T.; Davies, D
Butler, K. T.; Davies, D. W.; Cartwright, H.; Isayev, O.; Walsh, A. Machine learning for molecular and materials science.Nature 2018, 559, 547–555
2018
-
[58]
A generative model for inorganic materials design.Nature 2025, 1–3
Zeni, C.; Pinsler, R.; Zügner, D.; Fowler, A.; Horton, M.; Fu, X.; Wang, Z.; Shysheya, A.; Crabbé, J.; Ueda, S. A generative model for inorganic materials design.Nature 2025, 1–3
2025
-
[59]
S.; Gkagkas, K.; Tylianakis, E.; Froudakis, G
Fanourgakis, G. S.; Gkagkas, K.; Tylianakis, E.; Froudakis, G. E. A Universal Machine Learn- ingAlgorithmforLarge-ScaleScreeningofMaterials. Journal of the American Chemical Society 2020, 142, 3814–3822
2020
-
[60]
C.; Adler, P
Raccuglia, P.; Elbert, K. C.; Adler, P. D.; Falk, C.; Wenny, M. B.; Mollo, A.; Zeller, M.; Friedler, S. A.; Schrier, J.; Norquist, A. J. Machine-learning-assisted materials discovery using failed experiments.Nature 2016, 533, 73–76
2016
-
[61]
Perspective: Machine learning potentials for atomistic simulations.The Journal of Chemical Physics 2016, 145
Behler, J. Perspective: Machine learning potentials for atomistic simulations.The Journal of Chemical Physics 2016, 145
2016
-
[62]
Machine learning potentials for extended systems: a perspective.The European Physical Journal B2021, 94, 1–11
Behler, J.; Csányi, G. Machine learning potentials for extended systems: a perspective.The European Physical Journal B2021, 94, 1–11
-
[63]
On-the-fly active learning of in- teratomic potentials for large-scale atomistic simulations.The Journal of Physical Chemistry 35 Letters 2020, 11, 6946–6955
Jinnouchi, R.; Miwa, K.; Karsai, F.; Kresse, G.; Asahi, R. On-the-fly active learning of in- teratomic potentials for large-scale atomistic simulations.The Journal of Physical Chemistry 35 Letters 2020, 11, 6946–6955
2020
-
[64]
Chen, C.; Ong, S. P. A universal graph deep learning interatomic potential for the periodic table. Nature Computational Science2022, 2, 718–728
-
[65]
J.; Kornbluth, M.; Kozinsky, B
Musaelian, A.; Batzner, S.; Johansson, A.; Sun, L.; Owen, C. J.; Kornbluth, M.; Kozinsky, B. Learning local equivariant representations for large-scale atomistic dynamics.Nature Commu- nications 2023, 14, 579
2023
-
[66]
P.; Simm, G.; Ortner, C.; Csányi, G
Batatia, I.; Kovacs, D. P.; Simm, G.; Ortner, C.; Csányi, G. MACE: Higher order equivari- ant message passing neural networks for fast and accurate force fields.Advances in Neural Information Processing Systems2022, 35, 11423–11436
-
[67]
Mattersim: A deep learning atomistic model across elements, temperatures and pressures
Yang, H.; Hu, C.; Zhou, Y.; Liu, X.; Shi, Y.; Li, J.; Li, G.; Chen, Z.; Chen, S.; Zeni, C. Mattersim: A deep learning atomistic model across elements, temperatures and pressures. arXiv preprint arXiv:2405.04967 2024,
2024 arXiv
-
[68]
Phys- ical Review B2019, 99, 014104
Drautz,R.Atomicclusterexpansionforaccurateandtransferableinteratomicpotentials. Phys- ical Review B2019, 99, 014104
-
[69]
Atomic cluster expansion: Completeness, efficiency and stability.Journal of Computational Physics 2022, 454, 110946
Dusson, G.; Bachmayr, M.; Csányi, G.; Drautz, R.; Etter, S.; van der Oord, C.; Ortner, C. Atomic cluster expansion: Completeness, efficiency and stability.Journal of Computational Physics 2022, 454, 110946
2022
-
[70]
M.; Kovács, D
Batatia, I.; Benner, P.; Chiang, Y.; Elena, A. M.; Kovács, D. P.; Riebesell, J.; Advincula, X. R.; Asta, M.; Avaylon, M.; Baldwin, W. J. A foundation model for atomistic materials chemistry. arXiv preprint arXiv:2401.00096 2023,
2023 arXiv
-
[71]
L.; Bartók, A
Deringer, V. L.; Bartók, A. P.; Bernstein, N.; Wilkins, D. M.; Ceriotti, M.; Csányi, G. Gaussian process regression for materials and molecules.Chemical Reviews 2021, 121, 10073–10141
2021
-
[72]
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set.Computational Materials Science 1996, 6, 15– 50
Kresse, G.; Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set.Computational Materials Science 1996, 6, 15– 50
1996
-
[73]
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set.Physical Review B1996, 54, 11169
Kresse, G.; Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set.Physical Review B1996, 54, 11169
-
[74]
J.; Li, Z.; Csányi, G.; Walsh, A
Liang, X.; Klarbring, J.; Baldwin, W. J.; Li, Z.; Csányi, G.; Walsh, A. Structural Dynamics Descriptors for Metal Halide Perovskites.The Journal of Physical Chemistry C 2023, 127, 19141–19151
2023
-
[75]
A.; Rahm, J
Ångqvist, M.; Muñoz, W. A.; Rahm, J. M.; Fransson, E.; Durniak, C.; Rozyczko, P.; 36 Rod, T. H.; Erhart, P. ICET–a Python library for constructing and sampling alloy cluster expansions. Advanced Theory and Simulations2019, 2, 1900015
-
[76]
W.; Kaplan, A
Furness, J. W.; Kaplan, A. D.; Ning, J.; Perdew, J. P.; Sun, J. Accurate and numerically efficient r2SCAN meta-generalized gradient approximation.The Journal of Physical Chemistry Letters 2020, 11, 8208–8215
2020
-
[77]
Pytorch: An imperative style, high-performance deep learning library.Advances in neural information processing systems2019, 32
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L. Pytorch: An imperative style, high-performance deep learning library.Advances in neural information processing systems2019, 32
-
[78]
J.; Kohlmeyer, A.; Moore, S
Thompson,A.P.; Aktulga,H.M.; Berger,R.; Bolintineanu,D.S.; Brown,W.M.; Crozier,P.S.; In’t Veld, P. J.; Kohlmeyer, A.; Moore, S. G.; Nguyen, T. D. LAMMPS-a flexible simulation toolforparticle-basedmaterialsmodelingattheatomic, meso, andcontinuumscales. Computer Physics Communic...
2022
-
[79]
C.; Liang, X.; Walsh, A.; Menéndez- Proupin, E
Garrote-Márquez, A.; Lodeiro, L.; Hernández, N. C.; Liang, X.; Walsh, A.; Menéndez- Proupin, E. Picosecond Lifetimes of Hydrogen Bonds in the Halide Perovskite CH3NH3PbBr3. The Journal of Physical Chemistry C2024, 128, 20947–20956
-
[80]
Glazer, A. M. The classification of tilted octahedra in perovskites. Acta Crystallographica Section B: Structural Crystallography and Crystal Chemistry1972, 28, 3384–3392
-
[81]
All-inorganic CsPbBr3 perovskite: a promising choice for photovoltaics.Materials Advances 2021, 2, 646– 683
Ullah, S.; Wang, J.; Yang, P.; Liu, L.; Yang, S.-E.; Xia, T.; Guo, H.; Chen, Y. All-inorganic CsPbBr3 perovskite: a promising choice for photovoltaics.Materials Advances 2021, 2, 646– 683
2021
-
[82]
C.; Laurita, G.; Fabini, D
Schueller, E. C.; Laurita, G.; Fabini, D. H.; Stoumpos, C. C.; Kanatzidis, M. G.; Seshadri, R. Crystal Structure Evolution and Notable Thermal Expansion in Hybrid Perovskites Formami- diniumTinIodideandFormamidiniumLeadBromide. Inorganic Chemistry2018,57, 695–701
-
[83]
A.; Ceder, G
Deng, B.; Choi, Y.; Zhong, P.; Riebesell, J.; Anand, S.; Li, Z.; Jun, K.; Persson, K. A.; Ceder, G. Systematic softening in universal machine learning interatomic potentials.npj Com- putational Materials 2025, 11, 9
2025
-
[84]
A.; Ceder, G
Deng, B.; Choi, Y.; Zhong, P.; Riebesell, J.; Anand, S.; Li, Z.; Jun, K.; Persson, K. A.; Ceder, G. Overcoming systematic softening in universal machine learning interatomic poten- tials by fine-tuning.arXiv preprint arXiv:2405.07105 2024,
2024 arXiv
-
[85]
Näsström, H.; Becker, P.; Márquez, J.; Shargaieva, O.; Mainz, R.; Unger, E.; Unold, T. De- pendence of phase transitions on halide ratio in inorganic CsPb (Br x I 1- x) 3 perovskite 37 thin films obtained from high-throughput experimentation.Journal of Materials Chemistry A 20...
2020
-
[86]
M.; Levcenco, S.; Unold, T.; Taubert, A.; Schorr, S
Lehmann, F.; Franz, A.; Többens, D. M.; Levcenco, S.; Unold, T.; Taubert, A.; Schorr, S. The phase diagram of a mixed halide (Br, I) hybrid perovskite obtained by synchrotron X-ray diffraction. RSC Advances 2019, 9, 11151–11159
2019
-
[87]
Octahedral Tilt-Driven Phase Transitions in BaZrS3 Chalcogenide Perovskite
Kayastha, P.; Fransson, E.; Erhart, P.; Whalley, L. Octahedral Tilt-Driven Phase Transitions in BaZrS3 Chalcogenide Perovskite. The Journal of Physical Chemistry Letters 2025, 16, 2064–2071
2025
-
[88]
J.; Stokes, H
Howard, C. J.; Stokes, H. T. Group-theoretical analysis of octahedral tilting in perovskites. Structural Science 1998, 54, 782–789
1998
-
[89]
M.; Butler, K
Frost, J. M.; Butler, K. T.; Walsh, A. Molecular ferroelectric contributions to anomalous hysteresis in hybrid perovskite solar cells.Apl Materials 2014, 2
2014
-
[90]
Phase Transition Kinetics of MAPbI 3 for Tetragonal-to- Orthorhombic Evolution.JACS Au 2023, 3, 1205–1212
Wu, J.; Chen, J.; Wang, H. Phase Transition Kinetics of MAPbI 3 for Tetragonal-to- Orthorhombic Evolution.JACS Au 2023, 3, 1205–1212
2023
-
[91]
M.; McMahon, A
Leguy, A.; Frost, J. M.; McMahon, A. P.; Sakai, V. G.; Kockelmann, W.; Law, C.; Li, X.; Foglia, F.; Walsh, A.; O’regan, B. C. The dynamics of methylammonium ions in hybrid or- ganic–inorganic perovskite solar cells.Nature Communications 2015, 6, 1–11
2015
-
[92]
Exploring Librational Pathways with on-the-Fly Machine-Learning Force Fields: Methylammonium Molecules in MAPbX3 (X= I, Br, Cl) Per- ovskites
Bokdam, M.; Lahnsteiner, J.; Sarma, D. Exploring Librational Pathways with on-the-Fly Machine-Learning Force Fields: Methylammonium Molecules in MAPbX3 (X= I, Br, Cl) Per- ovskites. The Journal of Physical Chemistry C2021, 125, 21077–21086
-
[93]
H.; Siaw, T
Fabini, D. H.; Siaw, T. A.; Stoumpos, C. C.; Laurita, G.; Olds, D.; Page, K.; Hu, J. G.; Kanatzidis, M. G.; Han, S.; Seshadri, R. Universal Dynamics of Molecular Reorientation in Hybrid Lead Iodide Perovskites.Journal of the American Chemical Society2017, 139, 16875– 16884
-
[94]
Chen, Z.; Brocks, G.; Tao, S.; Bobbert, P. A. Unified theory for light-induced halide segregation in mixed halide perovskites.Nature Communications 2021, 12, 2687
2021
-
[95]
J.; Cho, J
Choe, H.; Jeon, D.; Lee, S. J.; Cho, J. Mixed or Segregated: Toward Efficient and Stable Mixed Halide Perovskite-Based Devices.ACS Omega 2021, 6, 24304–24315
2021
-
[96]
C.; Deng, Q.; Gomez, A.; Green, T.; Mankoff, J
Halford, G. C.; Deng, Q.; Gomez, A.; Green, T.; Mankoff, J. M.; Belisle, R. A. Structural Dynamics of Metal Halide Perovskites during Photoinduced Halide Segregation.ACS Applied Materials & Interfaces2022, 14, 4335–4343. 38
Reviewed August 6, 2026 · model on record in the stance chip above.
Discussion (0). Sign in to comment.